Open AccessPremises Reduction of Rule-Based Expert Systems Using Association Rules Technique
Author(s) -
Ahmed T. Sadiq
Publication year - 2021
Publication title -
magallaẗ kulliyyaẗ al-rāfidayn al-ǧāmi'aẗ al-'ulūm/maǧallaẗ kulliyyaẗ al-rāfidayn al-ǧāmiʻaẗ li-l-ʻulūm
Language(s) - English
Resource type - Journals
eISSN - 2790-2293
pISSN - 1681-6870
DOI - 10.55562/jrucs.v22i1.495
Subject(s) - association rule learning , expert system , premise , reduction (mathematics) , computer science , data mining , production (economics) , apriori algorithm , space (punctuation) , inference engine , knowledge base , a priori and a posteriori , association (psychology) , artificial intelligence , mathematics , linguistics , philosophy , geometry , macroeconomics , epistemology , economics , operating system
The heart of expert system is the knowledge base that determines the power of expert system and search engine space. The one important form of this knowledge is the production rule. The premises of these rules are the heart of production rules, therefore, the reduction of these premises is very useful to reduce the time and space in search engine. In this paper an approach will be presented to reduce the premises of production rules using association rules algorithm especially Apriori algorithm. Applying this approach gives a logical premise reduction which plays a good role in the search engine.